Using eye-tracking extends the potential scope of statistical learning research by providing information that mere manual reaction times (RTs) cannot. Our version can overcome the above-mentioned difficulties: it minimizes required motor responses and can measure statistical learning separately from other mechanisms. In this study, we aimed to develop the eye-tracking version of the widely used Alternating Serial Reaction Time (ASRT) task. A task that separates different aspects of procedural learning can contribute to more replicable and reliable findings. Second, some of the widely used tasks do not allow to separate different mechanisms that contribute to procedural learning thus, the measured performance does not solely reflect statistical learning (Nemethet al., Reference Nemeth, Janacsek and Fiser2013). First, statistical learning tasks often require manual responses (see, e.g., Howard & Howard, Reference Howard and Howard1997 Nissen & Bullemer, Reference Nissen and Bullemer1987 Schlichting et al., Reference Schlichting, Guarino, Schapiro, Turk-Browne and Preston2017), which adds noise to the measurement (Vakil et al., Reference Vakil, Bloch and Cohen2017) moreover, manual responses are infeasible with special target groups like infants or Parkinson’s disease patients (Koch et al., Reference Koch, Sundqvist, Thornberg, Nyberg, Lum, Ullman, Barr, Rudner and Heimann2020 Vakil et al., Reference Vakil, Schwizer Ashkenazi, Nevet-Perez and Hassin-Baer2021b). Although it has been widely researched for decades (Frost et al., Reference Frost, Armstrong and Christiansen2019), measuring statistical learning still faces difficulties. Procedural learning, among other cognitive mechanisms, requires recognizing and picking up probability-based regularities of the environment-a mechanism referred to as statistical learning (Armstrong et al., Reference Armstrong, Frost and Christiansen2017 Saffran et al., Reference Saffran, Aslin and Newport1996 Turk-Browne et al., Reference Turk-Browne, Scholl, Chun and Johnson2009). It underlies several everyday behaviors and habits, such as language, social, and musical skills (Lieberman, Reference Lieberman2000 Romano Bergstrom et al., Reference Romano Bergstrom, Howard and Howard2012 Ullman, Reference Ullman2016). Furthermore, it also enables future basic research to use a more sensitive version of this task to measure predictive processing.ĭeveloping perceptual and motor skills through extensive practice, that is, procedural learning is key to adapting to complex environmental stimuli (Simor et al., Reference Simor, Zavecz, Horváth, Éltető, Török, Pesthy, Gombos, Janacsek and Nemeth2019). Our method provides a way to apply the widely used ASRT task to operationalize statistical learning in clinical populations where the use of manual tasks is hindered, such as in Parkinson’s disease. We found robust, interference-resistant learning on RT moreover, learning-dependent anticipatory eye movements were even more sensitive measures of statistical learning on this task. We used the Alternating Serial Reaction Time (ASRT) task, adapted to eye-tracker, which, besides measuring reaction times (RTs), enabled us to track learning-dependent anticipatory eye movements. Here, we developed a new method to measure statistical learning without any manual responses. Statistical learning-the skill to pick up probability-based regularities of the environment-plays a crucial role in adapting to the environment and learning perceptual, motor, and language skills in healthy and clinical populations.
0 Comments
Leave a Reply. |